Drivers of customer touch point preference through different phases of shopping journey

THESIS MARKETING

MSc Business Studies – Marketing Track

“Drivers of Customer Touch Point Preference through Different Phases of Shopping Journey”

STATEMENT OF ORIGINALITY

This document is written by Jiaqi Mao who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.

ABSTRUCT

Customers, nowadays, are combining various touchpoints to complete their purchase process. This phenomenon, from one side, enriches the ways consumer experience can be optimized, while on the other side, makes it increasingly difficult for companies to focus limited resources on the most promising touchpoints. This study aims to examine the relationships between intrinsic and extrinsic motivations of customers and customer touchpoint preference during search, evaluation and purchase phases of shopping journey. By using an online survey study (N=396), this study investigates the customer touchpoint preference when shopping electronics and restaurant categories (Typical Search and Experience product). The survey participants are living in two countries (China and United States) with diverse demographic backgrounds (Age, Gender, Education level). Multiple Lineal regression analysis is applied to data collected. Our findings show that the drivers of touchpoint preference are different depending on the stage of the buying process and the specified product category considered.

Table of Contents STATEMENT OF ORIGINALITY ... ERROR! BOOKMARK NOT DEFINED. ABSTRUCT ... 3 1. CHAPTER 1 ‐ INTRODUCTION ... 5 1.1 RESEARCH BACKGROUND ... 5 1.2 RESEARCH STRUCTURE ... 10 2. CHAPTER 2 – LITERATRUTE REVIEW ... 10

2.1. MARKETING CHANNELS, MULTICHANNEL MARKETING AND OMNI‐CHANNEL MARKETING 10 2.2. TOUCHPOINTS IN OMNI‐CHANNEL ENVIRONMENT ... 13

2.3. DIFFERENT PHASES/STAGES OF SHOPPING ... 16 2.4. DRIVERS OF CUSTOMER MC USE IN DIFFERENT PHASES OF SHOPPING JOURNEY ... 21 2.5. LITERATURE GAP ... 24 2.6. THEORETICAL CONTRIBUTIONS ... 25 2.7. MANAGERIAL CONTRIBUTION ... 25 3. CHAPTER 3 – CONCEPTUAL FRAMEWORK AND HYPOTHESES ... 26 3.1. CONCEPTUAL FRAMEWORK ... 26 3.2. HYPOTHESES ... 28 3.2.1. Extrinsic Motivation ... 28 1) Perceived Usefulness: ... 28 2) Time Pressure ... 29

3) Perceived Product Utilitarian Value ... 30

3.2.2. Intrinsic Motivation ... 31 4) Perceived Ease-of-Use ... 31 5) Perceived Enjoyment ... 31 6) Hedonic Orientation ... 32 7) Product involvement ... 32 4. CHAPTER 4 ‐ RESEARCH DESIGN AND METHODOLOGY ... 33 4.1. SAMPLE CHOICE ... 34 4.2. PRODUCT CHOICE ... 35 4.3. MEASUREMENT AND OPERATION ... 35 5. CHAPTER 5 ‐ RESULT AND ANALYSIS ... 37 5.1. SAMPLE CHARACTERISTICS... 37 5.2. DATA PREPARATION ... 38 5.3. HYPOTHESES TESTING ... 39 6. CHAPTER 6 – DISCUSSION AND CONCLUSION ... 48 6.1. DISCUSSION ... 48 6.2. MANAGERIAL IMPLICATION ... 49 6.3. LIMITATION AND SUGGESTION FOR FURTHER RESEARCH ... 50 7. APPENDICES ... 51 7.1. FULL SURVEY ... 51 7.2. MEASURE SCALES AND ITEMS ... 54 7.3. SAMPLE CHARACTERISTICS ... 55

1. CHAPTER 1 - INTRODUCTION

1.1 Research Background

In the recent years, the number of channels for shopping has increased dramatically thanks to the fast development of digital technology, this has brought new challenges to companies (Verhoef, Kannan, & Inman, 2015). With the popularity of mobile devices (i.e. smart phone, tablet, smart watch, Google glass) among customers and the increasingly fast mobile networks (3G to 4G now), customers can now use the existing online channels and the newly added channels such as mobile specific apps anytime, anywhere. This transformation from using online channels only at a static place to mobilized usage of online channels has allowed customers to combine offline channels and online channels creatively in a way that fits the specific situation (Figure 1). For instance, according to Reuters, by the end of 2014, China had 649 million Internet users (50% of its population), with 557 million of those using handsets to go online. Meanwhile, both Alibaba and JD.com (first and second largest ecommerce platform in China) reported people shopping online increased by 20 percent in the year to the end of 2014. And the users of online payment services, operated by Alibaba and Tencent, increased by 17 percent in the year. Furthermore, according to a research by Google (Google, ipsos, & sterling, 2012), 41% of shopping events via smart phone were performed out of home. And 67% of shoppers start shopping on one device and continue on another. Although multichannel retailing has become a strategic priority for firms in every sector, particularly for brick-and-mortar retailers, the research on the field is quite

al. (2003), the retailer that does not integrate its channels correctly risks losing customers to competitors during the purchase process (Bendoly, Blocher, Bretthauer, Krishnan, & Venkataramanan, 2005)

Figure 1 The new multichannel shopping journey (Gartner, 2014)

In the competitive multichannel environment, retailers need to understand the definition of channel into more detail, instead of only online and offline, in order to integrate them properly. A channel is traditionally defined as a medium through which the firm and customer interact (Neslin et al., 2006). In the definition, the term channel does not include one-way communications such as TV and newspaper advertising and indirect contacts (i.e. Customers can share experience about the brand among themselves without interacting with the brand). This definition has expanded to touchpoint, which refers “an encounter type”, and an encounter is “a single episode of direct or indirect contact with the brand” (Baxendale, Macdonald, & Wilson, 2015). Direct touchpoints can be initiated either by customers (Store

versus Online) or by firms (i.e. In-store communication, In-website communication). Indirect touchpoints, on the other hands, are initiated by stakeholders other than the customers or firms (i.e. word of mouth, observing the behavior of other consumers and unpaid publicity) (Li, 2015). Most existing studies have focused on the preference of online and store touchpoint preference (Konuş, Verhoef, & Neslin, 2008). This research gap, related to studying direct and indirect touchpoints instead of online and store only is also emphasized by multiple recent researches (Baxendale et al., 2015; Peter C. Verhoef a,∗, P.K. Kannan b, J. Jeffrey Inman c, 2015)

It is also important to understand what drive the behavior of multichannel shoppers in order to motivate them to use the touchpoints, which the company has competitive advantage over the others. According to Harvard Business Review(Nunes & Cespedes, 2003), there are four kinds of buyers who have different customer touchpoint behaviors throughout the five stages of the purchasing process (Figure 2). For example, one study of brand loyalty for major household appliances found that 15% of appliance sales are habitual (another 12% are nearly habitual). Another study reported that 17% of French car buyers always purchase from the same makers.

Figure 2 Four Kinds of Buyers through shopping journey (Nunes & Cespedes, 2003)

Often, retailers encourage customers to shop from different channels, as there is evidence that multichannel customers provide higher revenues (Ansari, Mela, & Neslin, 2008; Kushwaha & Shankar, 2013; Venkatesan, Kumar, & Ravishanker, 2007). Although different channel usage drivers have been studied in the previous research (Neslin et al., 2006), it is yet unclear how do those drivers impact the touchpoints defined in this study.

My paper is based on the belief that in order to understand multichannel shopper behavior, it is necessary to take into consideration the stages of the purchase process. Early contributions, such as Alba et al. (1997), and Peterson et al. (1997), highlighted the need to investigate how consumers navigate across the Internet and Store during the search and purchase stages. We identify a research gap in analyzing whether or not the drivers of channel choice are different for each shopping stage. Most papers investigate the drivers of channel choice strictly at the purchase stage, a few papers have integrated the search and

purchase stages (Konuş et al., 2008; Verhoef, Neslin, & Vroomen, 2007) but to my knowledge there is no research that includes the evaluation stage. According to the five-stage customer decision journey model (Comegys, Hannula, & Väisänen, 2006; Nicholson, Clarke, & Blakemore, 2002), evaluation activities include setting rules to screen out unacceptable options to get a feasible choice set ranked by preference. Evaluation activities play a critical role for companies as they determine which product a customer will purchase out of large amount of competition products. Additionally, as a customer chooses to search in store or online, he or she encounters direct and indirect touchpoints which can have different importance levels for his or her evaluation activities. This is interesting to study because companies need to know which touchpoint customer prefer as well as how important certain touchpoints are for product selection. For instance, some customers can prefer to evaluate a restaurant considering WOM (Indirect) more important (Suggestions from a family, friend offline or online customer reviews) than direct channels such as product information on a restaurant website because WOM is better suited to evaluate experience products which are difficult to evaluate before consumption.

Our research question is thus “How do different drivers (Intrinsic and Extrinsic) lead to the touchpoint preference (direct & indirect) per stage of customer shopping journey (Search, Evaluation and Purchase). And how are these relationships influenced by product category and customer demographics?”

Thus the main contribution of my study is studying the drivers of direct and indirect touchpoint preference during search, evaluation and purchase phases. The choice of drivers

(i.e. usefulness, time pressure, perceived utilitarian value) and intrinsic (i.e. ease of use, enjoyment, hedonic orientation and product involvement). The research design of my paper presents two additional contributions: first, I analyze and compare two different product categories: restaurant as an example of experience goods, and consumer electronics as search goods, which are believed to have different shopping requirements; secondly, I collect data from two countries, i.e. China and United States, which show different degrees of penetration of Internet use and analyze the differences in touchpoint behavior between them.

1.2 Research Structure

The rest of the paper is structured as follows. In the next section we review the literature on the topic of Omni-channel marketing. Then we will give definitions on the touchpoints and shopping stages we will study. Lastly, we will investigate the drivers of channel preference per shopping stage. Afterwards, we present our research framework and hypotheses. Then we explain the methodology of the empirical study. Afterwards, the result will be presented. Lastly, explanation, conclusions and limitations of this study will be elaborated.

2. CHAPTER 2 – LITERATRUTE REVIEW

2.1.Marketing Channels, Multichannel marketing and Omni-channel marketing

A marketing channel is defined as a customer touch point or a medium through which the firm and customer interact (Neslin et al., 2006). This definition ignores the indirect channels through which the firm and customers do not interact directly such as word of mouth. This definition is expanded in our research to touchpoint, which refers “an encounter type”, and an encounter is “a single episode of direct or indirect contact with the brand” (Baxendale et al., 2015). Direct channels are the channels the brand and its channel partners (such as

retailer) interact directly with customers while indirect channels are the ones customers do not interact with the brand and channel partners (Such as peers). Moreover, another research clarifies the function of channels by defining a channel to be a business structure, reaching from the point of product origin to the customer, through which a manufacturer or marketer motivates, communicates, sells, ships, stores, delivers, and services the customer's expectations and the product's needs (McCalley, 1996). So we propose that, by combining the two definitions, a channel is a single episode of direct or indirect contact with the brand through which companies motivates, communicates, sells, ships, stores, delivers, and services the customer’s expectations and the product’s needs.

Furthermore, in order to manage the diverse channels along the customer shopping process, multichannel marketing emerges. According to Neslin et al., multichannel marketing is defined as “the design, deployment, coordination, and evaluation of channels to enhance customer value through effective customer acquisition, retention, and development.” (Neslin et al., 2006). Previously, studies on multichannel marketing have mainly considered offline channels (stores), online channels (i.e., Web store), and traditional direct marketing channels, such as catalogs. For example, Verhoef et al studied the “research shopping” phenomenon, where shoppers search in one channel (i.e. online) and purchase in another channel (i.e. Store) (Verhoef et al., 2007). Similarly, Konus et al. discussed a multi-channel segmentation solely considering these multi-channels(Konuş et al., 2008). Moreover, there are abundant researches on the effect of online channel additions and online channel migration to share-holder value, store sales, customer purchase behavior, customer profitability and/or customer loyalty(Ansari et al., 2008; Leeflang, Verhoef, Dahlström, &

Steenburgh, Deighton, & Caravella, 2012). The reason why these general channel types have been the focus of multi-channel researches in the past decades is that, with the increasing popularity of online channels among populations, it is important to understand how does the growth of the online channels impact firms and customers using traditional channels such as stores and catalogues.

Under the same logic, the fast development of mobile devices has lead to another disruptive change in the retail environment which requires understanding how this mobile devices will impact firms and customers using stores, catalogues and current online channels (Rigby, 2011). This change requires a new multichannel approach called the omni-channel approach, which emphasizes deep level integration of different channels to provide the customer with seamless shopping experience through any channels accessed from any devices. Similar to online channel development, there has been research on the effect of mobile channels and specifically mobile apps on performance (Xu, Forman, Kim, & Van Ittersum, 2014).

Table 1 Definitions of Multichannel environment and examples

Topics Definition Example

Touchpoints

A single episode of direct or indirect contact with the brand through which companies motivates, communicates, sells, ships, stores, delivers, and services the customer’s

expectations and the product’s needs.

Store, Online, In-store Communication, In-Website Communication, Online Review, Observing others Multichannel Marketing

The design, deployment, coordination, and evaluation of channels to enhance customer value through effective customer acquisition, retention, and development

How does the growth of the online channels impact firms and customers using traditional channels such as stores and catalogues

Omni-channel Marketing

New multichannel approach, which emphasizes deep level integration of different channels to provide the customer with seamless shopping experience through any channels accessed from any devices

How do mobile devices impact firms and customers using stores, catalogues and current online channels (Rigby, 2011)

2.2.Touchpoints in Omni-channel environment

In the Omni-channel environment, integration of different channels becomes crucial. With the abundant researches on online and store channel preference during search and purchase phase, it becomes interesting to study what touchpoints play an important role in making product choice when customers evaluate the product quality online and in store. According to Brand XP by TNS(TNS, 2014), touch points are NOT neutral and have a different value. The same message will have a different effect when sent by mail, displayed on a website, aired on TV or told by a friend. Furthermore, TNS provides a comprehensive list of touchpoints in different categories and their relative influence on its ability to inform, to make brands attractive and help purchase decision based on a study in Belgium (Figure 3). As seen in Figure 3, there are various ways to classify touchpoints and each touchpoint has different influence over customers of a certain industry (in this case the restaurant chain category)

Figure 3 Most influential 34 touchpoints in restaurant chain category (Brandxp study, 2014)

The POE classification was first introduced by Forrester Research Inc. Touchpoints can be classified into Paid (Brands pay to leverage a channel), Owned (Channel a brand controls) and Earned (When customers become a channel). Owned touchpoints exists to build long

term relationships with existing potential customers and earned media (i.e. Website, mobile site, company blog, company twitter account). Paid touchpoints drive traffic to both owned and earned touchpoints (i.e. display ads, paid search). Both owned and paid touchpoints allow brands to interact with customers directly, while earned touchpoints are customer oriented without the direct involvement of the brands in the interaction (i.e. WOM, Buzz, Viral).

Table 2 POE touchpoint classification Touchpoint classification by

Forreseter Description

Paid Brands pay to leverage a channel

Owned Channel a brand controls

Earned When customers become a channel

Moreover, according to Li et. al 2015 (Li, 2015), touchpoints can be classified into direct and indirect based on whether the brand is directly involved in the interaction (See Figure 4). The research by Mckinsey 2009 validated the classification stating that two-thirds of the touch points during the evaluation phase involve customer-driven marketing activities, such as online reviews and word-of-mouth recommendations from friends and family (Other-initiated touchpoints) as well as online and store usage (Customer-initiated touchpoints). A third of the touchpoints involve company-driven marketing (Firm-initiated touchpoints) (David Court, Dave Elzinga, Susan Mulder, and Ole Jørgen Vetvik, 2009).

Figure 4 Touchpoint Typology studied in this research (Li, 2015)

In order to integrate the touchpoints during customer shopping process, it is crucial to look into different phases of shopping. Different touchpoints can serve different purposes during different stages of shopping process. Thus one can only study channel preference by looking into the differences of shopping stages.

2.3.Different phases/stages of shopping

In the omni-channel environment, the “research shopping” phenomenon has evolved. Showrooming (Mehra, Kumar, & Raju, 2013) is becoming an important issue. Customers frequently search in store and simultaneously search on mobile to get more information and may decide to buy somewhere else while in store. Opposite to showrooming, webrooming (Buss, 2015) is also becoming popular as customers search information online and simultaneously buy in store. These developments have led to an increased use of the Brick-Mortar (BM) store solely as an information channel to establish definitively a customer’s best-fit product. For instance, in the vacation industry, 30% of the consumers use one channel for search and a different channel for purchase (Yellavali, Holt, & Jandial, 2004).

Kelly (2002) reports that roughly half of online shoppers research the product on the Internet and then purchase it in a brick-and-mortar store. Thus it has become crucial that a company understands what touchpoints customers prefer within the customer shopping process so that a customer does not easily switch to competition offer. The shopping process, at least for products that require medium to high involvement on the part of customers, consists of distinct stages (Lilien, Kotler, & Moorthy, 1992). Traditionally, a shopping process can be broadly classified into Search, Purchase and After-sales phases (Konuş et al., 2008). This classification of shopping phases is too generic to illustrate the specific connection between specific shopping phases and specific touchpoints as customer journey is becoming increasingly complex and customers need different touchpoints for the specific part of shopping journey.

Both Google and P&G have studied on the Moments Of Truth (MOT) customer shopping journey (Cohen, 2013). Google introduced the Zero Moment of Truth, which emphasizes the initial needs recognition and then search information online for a potential purchase. Based on Google’s research, people checked 10.4 sources of information to make a decision in 2011, an increase from 5.3 sources in 2010. Furthermore, P&G complement it by extending to the first, second and third moment of truth, which emphasize the Moment of decision point to buy a specific brand or product, Moment of after-purchase and using the product and the Moment of becoming a true fan & giving back to your brand with new content: word of mouth, ratings and reviews (Figure 5)

Figure 5 Marketing's 4 Moments of Truth (HeidiCohen, 2013)

Furthermore, researchers agree that customers form consideration sets, usually consisting of about five products, early in the purchase process (Nedungadi, 1990). Then, based on further evaluation of the products in their consideration sets, they select particular products (or sets of products) for purchase. This is aligned with the 5-step customer decision model, which adds needs recognition, evaluation and post-purchasing stages (Comegys et al., 2006; Nicholson et al., 2002). Customers’ objectives influence their choice of channels, and their objectives may differ by stages (Balasubramanian, Raghunathan, & Mahajan, 2005). Below I describe each shopping stage briefly.

 In the Need Recognition (NR) stage, the goal of customers is to sense the difference between their current state and the state they desire so they would like to start a shopping process. This need to buy can be triggered by internal (i.e. Situational, Occasional, Specific Purpose such as hunger, thirst) or external stimuli (i.e. Looking at a friend's purchase, word of mouth, ads). It can also be triggered by impulse in which shopping happens as a

by-process or window-shopping. This research does not consider the channel preference at this stage as the scope limits to the consideration of the shopping processes initiated by internal stimuli.

 In the Information Search (IS) stage, depending on the need recognized in the previous stage, customers use different channels to collect information on available products to create a consideration set. The role of channels in this stage is to deliver relevant information to customers efficiently and effectively. Companies competing for the customers aim to be entering the consideration set. In this research, customers make channel preference choice between online and store (consumer-initiated touchpoints) at this shopping stage.

 During Evaluation Alternative (EA) stage, customers set rules to screen out unacceptable options so that they will have a feasible choice set, which is ranked based on preference. Concerning resource limit (i.e. time or energy), customers aim to stop searching and evaluating and move to purchase stage when they would rather spend their time somewhere else. So they consider the value of extra evaluation and the value of doing something else(Urban, Weinberg, & Hauser, 1996). From company perspective, companies aim to be in the choice set and ranked high in the choice set so that their products are more likely to be purchased. When customers are searching online or in-store, they encounter firm or other initiated touchpoints, which have different impact on product purchase decision.

 During Purchase Decision (PD) phase, customers may not always buy the product that is ranked on the first place. The purchase decision is influenced by the attitudes of others and other situational factors such as point of sale, time of purchase, volume of purchase, and

 In the Post-Purchase (PP) phase, customers start using the product or service. If companies want the customer to buy again or refer to others, they need to understand the post-purchase behavior, which can be divided into post-purchase satisfaction and post-purchase actions. Theoretically, satisfied customers are more likely to repurchase and recommend to others while unsatisfied customers are more likely to stop buying and complain. In order to direct satisfied customers back into the loop of another shopping journey with the company and have their positive voice heard by more people and minimize the impact of their negative voice, companies need to know which channels customers use at this stage. This shopping stage is out of the scope of this study.

Most previous research does not account for the fact that customers pass through different phases across the shopping process, although strengths and weaknesses of touchpoints typically vary in these phases. Studies so far have mostly focused on the search and purchase phases (Verhoef et al., 2007), but have neglected the after-sales phase(Konuş et al., 2008). While studying search and purchase phases, this research will investigate evaluation phase separately from the search phase.

Table 3 5-Stage Customer shopping journey and focus of this study Shopping

stage name

Brief Description Focus in

this research Need

Recognition (NR)

Sense the difference between their current state and the state they desire so they would like to start a shopping process

No

Information Search (IS)

Collect information on available products to create a consideration set

Yes

Evaluation Alternative (EA)

Customers determine the effort dedicated to screen out unacceptable options (5 stage model). Decision point to buy a specific brand or product (First Moment of Truth by P&G)

Yes

Purchase Decision (PD)

Post-Purchase (PP)

Customers start using the product or service and customers become funs (Second & Third Moment of Truth by P&G)

No

2.4.Drivers of customer MC use in different phases of shopping journey

Abundant researches exist to study the determinants of channel selection. It was argued that marketing activities such as incentives (Kushwaha & Shankar, 2013) by companies can drive customers to choose certain channel. Moreover, the characteristics of the channel itself such as ease-of-use, price, service and risk have significant impact over channel choice (Verhoef et al., 2007). Furthermore, channel integration was studied by Montoya-Weiss et al. (2003) to impact channel choice. Situational factors and social influence can also influence channel choice(Nicholson et al., 2002). Lastly, many authors argue that individual difference among customers have significant impact over channel choice. These include variables such as demographics (Ansari et al., 2008; Kushwaha & Shankar, 2013; Verhoef et al., 2007), previous experience (Keen et al. (2004); Meuter et al. (2000); Inman et al. (2004)), and stage in life cycle (Thomas and Sullivan (2005a))

This study chooses channel drivers based on Technology Adoption Model supported by Motivation Model. The use of the Internet for shopping has been often explained with the TAM Model (Davis, 1989), which includes usefulness and ease-of-use as main determinants of the acceptance of a technology for developing a task. However, the model has ignored other variables that can have an impact on touchpoint preference. The

prefers using certain channels to achieve rewards external to the system (Enjoy the result), while an intrinsically motivated shopper uses certain channel driven by the benefits derived from the interaction with the channel (Enjoy the process). Based on transaction costs economics, I expect that the drivers of channel preference function differently at each stage of shopping. There are different types of transaction costs involved across the shopping process and the online channel lowers the costs at the search stage but raises the examination and payment costs. Balasubramanian et al. (2005) developed a model of product and process utilities that analyzed channel choice considering three stages of shopping: forming a consideration set, product evaluation and purchasing. They discovered that, for the first two stages, the Internet is particularly useful as it lowers information-search costs. For purchasing, they suggested that the online channel will be chosen when customers want to minimize transaction costs, and the offline channel when there is perceived risk and a preference for immediate consumption (Balasubramanian et al., 2005). Except for the drivers for online and store, which have been the focus of most existing studies, the extrinsic motivation such as perceived product utilitarian value versus hedonic value drives the preference of direct versus indirect channels, as it determines what kind of criteria customers use to make purchase intention (Kim, 2006; Park & Moon, 2003). According to Park & Moon et al 2003, customers who buy or use a particular product to satisfy their utilitarian needs behave carefully and are efficiently oriented to the problem solving while hedonic oriented customers use subjective criteria which is not explained by concrete or objective attributes.

This study also considers demographics drivers such as gender, age, education level and country of residence. Herrero Crespo and Rodriguez del Bosque (2010) (Crespo & del Bosque, 2010) found that gender and education affect the importance of drivers of

channel choice for consumer electronics, but age and occupation were not so relevant. Venkatesan et al. (2007) (Venkatesan et al., 2007) found that gender affects the adoption of multiple channels, with male customers being more likely to adopt a new channel. Kushwaha and Shankar (2013) (Kushwaha & Shankar, 2013) found that multichannel shoppers were younger than offline customers and online-only customers were younger than multichannel shoppers. Contrary to most research evidences, Konus" et al. (2008) did not find significant differences between channel-based segment regarding demographics. Badrinarayanan et al. (2012) (Badrinarayanan et al., 2012) found that culture affects multichannel behavior

Product characteristics also impact the preference of touchpoints for shopping. Peterson et al. (1997) (Peterson, Balasubramanian, & Bronnenberg, 1997) stressed the need to explicitly include product characteristics when evaluating the impact of the Internet for searching and purchasing. When considering the stages of multichannel shopping, researchers have noted that the utility of one channel for a specific stage depends on the product category (Balasubramanian et al., 2005). According to the information economics perspective (Mandeville, 1998), there are different degrees of assessment difficulties of technological information for different product categories. On the two extreme sides, products can be classified into Search (low assessment difficulty) and Experience products (high assessment difficulty). Moreover, customer knowledge impacts assessment difficulty. The more knowledge customer has about a product, usually the easier to assess the quality of a product. This means that different customers

product. Teo and Yu (2005) (Teo & Yu, 2005) found that the difficulty of physically assessing product quality affects online buying intentions. For experience goods such as restaurant, the traditional offline touchpoint such as restaurant itself may be preferred for purchasing as the qualities of the product can be fully evaluated and risk can be reduced, whereas for search goods such as consumer electronics, whose features can be evaluated objectively, the online touchpoint may be more efficient to search and compare alternatives and make the purchase decision.

Table 4 Touchpoint preference drivers Types of

motivations

Drivers Description

Extrinsic Motivations

Perceived Usefulness Online improves shopping efficiency Time Pressure Time as a scarce and valuable resource Perceived Product

Utilitarian Value

Evaluate product based on concrete indicators to solve a problem Intrinsic

Motivations

Perceived Ease-of-Use The effort that needs to be put into shopping process

Perceived Enjoyment The fun to use online channels Hedonic Orientation Pleasure encountered in the task of

shopping

Customer Involvement Involvement with the product category 2.5.Literature Gap

According to literatures that have been collected, it has proven that customers are combining touchpoints creatively along different phases of shopping journey thanks to the advancement of digital technology in the omni-channel environment. Customers choose to search, evaluate and purchase with different touchpoints because of certain intrinsic and extrinsic motivations. Despite the fact that, abundant researches exist on the drivers of channel preference, there are still limited studies that examine how do the impact of these drivers change in different shopping phases. When evaluating products, they make product choice based on different touchpoints they experience while being online or in store. Most existing studies have investigated the preference of online and store touchpoints while few

have considered the importance of direct and indirect touchpoints in combination with online and in store. Therefore, In order to fill the literature gaps, this research aim to address following questions:

“How do different drivers (Intrinsic and Extrinsic) lead to the touchpoint preference (direct & indirect) per stage of customer shopping journey (Search, Evaluation and Purchase). And how are these relationships influenced by product category and customer demographics?”

2.6.Theoretical Contributions

As explained above, the research studies how does the impact of intrinsic and extrinsic drivers on the preference of different touchpoint types change by shopping stage. This research studies multiple touchpoint types while most existing researches focus on online and store. Furthermore, while most other researches have overlooked the evaluation phase, this study pays particular attention on how the importance of direct and indirect channels differ online and in store and by product characteristics when customers conduct evaluation activities. Furthermore, the research studies the impact of product category and shopper demographics on the relationships.

2.7.Managerial Contribution

Out of tremendous amount of touchpoints online and offline, managers need to decide which ones fit the company the best so that they can allocate limited resources

can understand that customers can actually combine different touchpoints during shopping and each touchpoint can serve different purpose at each shopping stage. For example, by knowing that its target customers, driven by certain motivations, prefer searching online to purchase electronics, managers can spend more resources (money & labor) on developing its online presence. Moreover, by knowing that the target customers consider direct touchpoints such as official or retailer website more important than consumer reviews, the company can prioritize the focus on enriching the quality of the website instead of spending lots of effort on consumer review platforms. Furthermore, the study has valuable relevance for channel migration campaigns of multichannel retailers, as managers may prefer to drive different target groups to different shopping paths, which optimize the shopping experience.

3. CHAPTER 3 – CONCEPTUAL FRAMEWORK AND HYPOTHESES

3.1.Conceptual Framework

The research design tries to explain channel preference defined under omni-channel environment for three stages of shopping by looking at the variables that define channel-based segments (See table 3). The conceptual framework is developed to illustrate the relationships between the elements discussed in the literature review. Channel preferences are the outcome variable of this study. The intrinsic and extrinsic motivations are the predictor variables for channel preferences. Furthermore, product category and shopper demographics are expected to play moderator role on the relationships.

Figure 6 Conceptual framework by the author

indirect touchpoints in store. When searching online, I consider in-website communication as direct touchpoint and online reviews as indirect touchpoint. In the purchase phase, my focus goes back again to store and online touchpoints. Furthermore, I classify product under study into research and experience product to examine their different impact over the relationships. Meanwhile, shopper demographics could also impact the relationships.

Table 5 Research Framework Variable Overview

Sub- Question Independent Variable (What I change) Dependent Variables (What I observe) Controlled Variables (What I keep the same) How does the

impact of intrinsic and extrinsic drivers on the preference of online versus store touchpoint change from search to purchase stage?  Usefulness  Time Pressure  Ease of Use  Enjoyment  Hedonic  Product Involvement  The level of online touchpoint preference versus store  Product Category  Shopper Demograph ics

How does the intrinsic and extrinsic drivers impact the preference of direct and indirect touchpoints online and store?  Perceived product utilitarian value  Direct/Indirect touchponts online; Direct/Indirect touchponts in store  Product Category  Shopper Demograph ics

In this section, the hypotheses over the relationships within the above conceptual framework will be explained into detail.

3.2.Hypotheses

3.2.1. Extrinsic Motivation 1) Perceived Usefulness:

The perceived usefulness is defined as the belief that the online channel improves the efficiency of the shopping process. Perceived usefulness has often been included in models to explain intentions to purchase from online channels, with most of the studies

showing a positive relationship (Ha & Stoel, 2009). During the search stage, the online channel is particularly useful as it allows easy comparison and evaluation of alternatives, which lowers information search costs(Balasubramanian et al., 2005) and increases the attractiveness of the channels(Verhoef et al., 2007). On the contrary, when the perceived usefulness is extremely low, customer will prefer to only use the physical stores to search and purchase. Thus, we hypothesize:

 H1a: A higher level of perceived usefulness is positively associated to the preference of the online (vs. store) for search.

 H1b: A higher level of perceived usefulness is positively associated to the preference of the online (vs. store) for purchase.

2) Time Pressure

Time pressure, defined as the consideration of time as a scarce and valuable resource by customer, may have an effect on channel choice for all the phases of the shopping process. Generally speaking, time-pressed customers will have less time searching and will look for convenient ways to purchase the products, thus time pressure acts as an extrinsic motivation as it relates to the desire to accomplish the shopping task fast. Staying in a single channel can be a way to save time, for that reason, Konus et al. (2008) predicted that customers pressed for time would be less likely to shop in multiple channels across the stages of the shopping process. However, they did not find a significant effect for this relationship. Previous research reveals that customers, specially ‘‘hurried customers”, perceive online shopping to be a time saving practice (Alreck & Settle, 2002). In the context of multichannel retailing,

(vs. the store) for search.

 H2b: A higher shopper time pressure is negatively associated to the preference of the online (vs. the store) for purchase.

3) Perceived Product Utilitarian Value

Based on the type of benefits that customers seek, the same search or experience products can be classified into utilitarian and hedonic goods. Function or performance is emphasized by utilitarian product and pleasure or self-expression is emphasized by hedonic product. A perceived high utilitarian value of a product means that the product is useful to solve a specific problem. Thus, consumers who buy or use a particular product to satisfy their utilitarian needs behave carefully and are efficiently oriented to the problem solving(Babin, Darden, & Griffin, 1994). Whether a particular product is utilitarian or hedonic is decidedly based upon a consumer’s subjective judgment about the product’s value(Park & Moon, 2003). According to information economics theory(Mandeville, 1998), direct channels provide straightforward information on the product features that consumers can evaluate from company perspective while indirect channels provide experiential information from other customers’ perspective (David Court, Dave Elzinga, Susan Mulder, and Ole Jørgen Vetvik, 2009). For both online and in store, these types of information are easily available for customers in the omni-environment, so I do not expect difference of importance between online and store. To sum up, as different direct and indirect touchpoints provide revalidation of the quality of a product from different perspective and it is easily available online and in-store, high utilitarian oriented customers will consider both direct and indirect channels important for product choice. So we hypothesize that

 H3a: The higher perceived product utilitarian value is positively associated to the perceived importance of direct touchpoints for evaluation both online and in store.

of indirect touchpoints for evaluation both online and in store.

3.2.2. Intrinsic Motivation 4) Perceived Ease-of-Use

In contrast to usefulness, which relates to the outcomes of the process, perceived ease-of-use relates to the effort that requires to be put into the shopping process. Perceived ease-of-use of the online channel is defined as the degree to which the customer believes using the Internet for shopping will require little effort. Although customers have learned to navigate the websites of companies, Rose et al. (2012) (Rose, Clark, Samouel, & Hair, 2012)conclude that ease-of-use of the online store continues to be an important factor in influencing customer attitude and behavior for purchasing online. Verhoef et al. (2007) found that attributes related to ease-of-use influence the attractiveness of the channels used for searching.

 H4a: A higher level of perceived ease-of-use is positively associated to the preference of the online (vs. the store) for search.

 H4b: A higher level of perceived ease-of-use is positively associated to the preference of the online (vs. the store) for purchase.

5) Perceived Enjoyment

Ignoring the hedonic aspect of using the Internet can be a major omission (Childers, Carr, Peck, & Carson, 2002). Perceived enjoyment refers to the extent the customer thinks using the online channel for shopping is fun and pleasant in itself. Previous research has observed that perceived enjoyment affects the attractiveness of the Internet for purchasing

 H5a: A higher level of perceived enjoyment is positively associated to the use of the online (vs. the store) for search.

 H5b: A higher level of perceived enjoyment is positively associated to the use of the online (vs. the store) for purchase.

6) Hedonic Orientation

Contrary to the perceived enjoyment variable, this is a motivation that does not refer specifically to enjoyment of the online channel, but to the pleasure encountered in the task of shopping. Overby and Lee (2006) (Overby & Lee, 2006)found that customers with higher hedonic motivations prefer the online retail channel for purchasing. On the contrary, To et al. (2007) al(To, Liao, & Lin, 2007) concluded that a hedonic orientation would drive offline search and purchase. Konus et al. (2008) analyzed segments formed on the basis of attitudes towards channels for search and purchase and found that a hedonic orientation is related to the use of the channels, as the segment of multichannel shoppers are the ones with a higher hedonic orientation. Schröder and Zaharia (2008) (Schröder & Zaharia, 2008) found that customers who use store channels to seek information and purchase have a higher hedonic orientation than shoppers of online channels. We are inclined to believe that hedonic shoppers will prefer offline to online shops for searching and purchasing since those individuals do not value the higher convenience offered by the online channel but the increased opportunities for enjoyment offered by offline channels.

 H6a: A higher shopper hedonic orientation is negatively associated to the use of the online (vs. the store) for search.

 H6b: A higher shopper hedonic orientation is negatively associated to the use of the online (vs. the store) for purchase.

7) Product involvement

multichannel behavior(Kumar & Venkatesan, 2005). Shoppers that are more involved in the product category will get rewarded from the process of shopping in itself. Highly involved customers search and compare more information before making a purchase (Balabanis & Reynolds, 2001), so product involvement would affect the use of the channels. Balasubramanian et al. (2005) suggest that customers with expertise in the product category are able to make decisions relying only on the factual information provided online. Wolfinbarger and Gilly (2001) (Wolfinbarger & Gilly, 2001)explored the role of involvement with the product category as an experiential motivation to search and purchase online and found that online shoppers show higher levels of product involvement. In the apparel context, Jones and Kim (2010) obtained a strong positive association between clothing involvement and online apparel purchasing intention. In fact, as highly involved shoppers have made investments in knowledge of the product category the transaction costs involved in performing shopping activities online decrease, making shoppers more likely to use the online channel.

 H7a: A higher product involvement is positively associated to the use of the online (vs. the store) for search.

 H7b: A higher product involvement is positively associated to the use of the online (vs. the store) for purchase.

With each relationship hypothesized above, it is also interesting to study the effect of product categories and demographics. In the next chapter, the research method used to test the hypotheses will be explained including the sample, choice of product categories, and the

research will be carried out using a cross-sectional survey design because this is the most common design in this field, and it is useful for using statistics to test hypotheses and generalize the findings. Firstly, the most evident characteristics of the collected sample will be outlined. Afterwards, the variables included in the questionnaire and corresponding reliabilities are discussed. Finally a brief description will be given of the statistical approach that was taken in order to test for the expected relationships as discussed in the previous chapter. See the appendix for the complete questionnaire

4.1.Sample choice

The sample consisted of residents in United States and China, who have diverse demographic backgrounds. The two countries are chosen first because they are the number one and two biggest economies in the world with lots of interest to study. Secondly, these two countries differ significantly in the overall penetration of internet use among population (China: 47.9% reported by the China Internet Network Information Center (CNNIC) and United States: 84% reported by Pew Research Center). Thirdly, these two countries differ significantly in the penetration rate of online shopping. It has been reported that over 69% of US online adults shop online at least monthly with 33 percent shopping online every week in 2015 according to Mintel’s Online Shopping US 2015 report. While in China, 361.42 million (27.8% of the population) people had purchased goods online according to Statista). Lastly, both countries have rather large cultural differences according to the hofstede cultural dimensions. So it is likely to observe differences driven by country factors. At least 200 Local Chinese in mainland China using convenient sampling method (Recruited via social media of the researcher and the help of friends and family) and 200 participants mainly living in United States are recruited to participate the survey using random sampling (Recruited via an online panel in the US without restricting any sample criteria)

4.2.Product choice

This study selects consumer electronics (computers, smartphones) and food (Dinning in restaurants or delivery) categories as the focus of research. The product category choice in this research needs to be diversified in terms of evaluation difficulty before consumption. According to information economics perspective, direct and indirect touchpoints differ in terms their capabilities to allow customers to effectively assess the quality of different product categories (Search versus Experience). For instance, search products such as smart phones can be evaluated effectively based on factual information before buying it while it is more effective to assess the quality of a restaurant based on consumer reviews because customers are difficult to judge the quality fully before tasting the food. Furthermore, these categories differ in terms of purchase frequency and complexity and retailers’ branding and distribution strategies and should provide variation in the consumer perceptions of the

channels.

4.3.Measurement and operation

In terms of dependent variables, the questionnaire (Appendix 10.1) will ask respondents for their preference of online versus store in the search and purchase phases. For evaluation phase, respondents are asked about their perceived importance of in-store communication, observing others and using mobile to read online reviews while in store. And they are asked about their perceived importance of in-website communication and online reviews while searching online. In regards to predictor variables, I use validated reliable items to measure

A pilot test was conducted to make sure the survey is properly understood. The author invited 6 different English-speaking participants to do the survey face to face and then asked each of them to explain their understanding of each question after filling out the questionnaire. The author improved the question validity by matching the intended meaning per question by the author with the interpreted meaning per question by the pilot test participants. The same approach is applied to ensure the quality of the translated version. The author tested the questionnaire with 3 local Chinese with different education backgrounds (High school, university and PHD) and asked them to explain their understanding of the survey in Chinese. During the pilot test, the average time spent per questionnaire is 6 minutes for both language versions.

Table 6 Summary of variables & operationalization

Variable Operationalization Reliabilit y (Cronbac h’s Alpha) Usefulness Davis (1989), Childers et al. (2001)

• Shopping via the Internet allows me to shop faster • Shopping via the Internet is useful for me

• Shopping via the Internet makes my life easier 0.81 Time pressure Konus’

et al. (2008)

• I am always busy

• I usually find myself pressed for time 0.84

Ease-of-use Davis (1989), Rose et al. (2012)

• Internet shopping allows me to easily shop for what I want

• It is easy to become confident at Internet shopping 0.78

Enjoyment Childers et al. (2001), Cha (2011)

• Shopping via the Internet is enjoyable • Shopping via the Internet is pleasant

• Shopping via the Internet is interesting 0.88

Hedonic orientation Konus’ et al. (2008)

• I like shopping for apparel and accessories/electronics

• I take my time when I do shopping for

Product involvement Kressmann et al. (2006)

I am highly involved in electronics products. I feel like I am an expert in electronics.I am more interested in electronics in comparison to other

people 0.9 Perceived Product

Utilitarian Value

My electronics buying decision-making is made primarily based on functional perspective.

1 Item N/A

To analyze the data collected, I chose to use multiple regression analysis method because regression analysis is a technique that examines the linear relation between one or more Independent variables (Quantitative) and one Dependent variable (Quantitative) (Blumberg, Cooper, & Schindler, 2008). All my independent and dependent variables are measured at 1-5 likert scale, which can be analyzed as if they were numerical interval data (Blumberg et al., 2008). And my hypotheses need to be tested by predicting dependent variable based on multiple independent variables.

5. CHAPTER 5 - RESULT AND ANALYSIS

5.1.Sample Characteristics

The total sample size is 396. Regarding the characteristics of sample, 53.8% are living in China and all the rest are living in United States. As for the gender, 60.6% of all collected respondents are female while 39.4% of them are male. The data were also collected from highly educated respondents as 296 of them are either holding a bachelor degree, a master degree or a doctor degree (50%, 7.8% and 16.9% respectively). Lastly, most of the sample are rather young as 82.3% are under 44, among which, 53.8% are younger than 34.

5.2.Data Preparation

First of all, by generating the frequency table for the demographics, I checked the frequencies to examine if there were any errors in the data. There were no errors found. There were also no missing values in the data as all the questions were compulsory to answer in order to submit the questionnaire.

What is your gender?

Frequency Percent Valid Percent

Cumulative Percent

Valid Male 156 39.4 39.4 39.4

Female 240 60.6 60.6 100.0

Total 396 100.0 100.0

What is your approximate age? Frequency Percent Valid Percent

Cumulative Percent

Valid 17 and less 3 .8 .8 .8

18-24 36 9.1 9.1 9.8 25-34 174 43.9 43.9 53.8 35-44 113 28.5 28.5 82.3 45-54 44 11.1 11.1 93.4 55-64 21 5.3 5.3 98.7 65 and more 5 1.3 1.3 100.0 Total 396 100.0 100.0

What is your highest level education Frequency Percent Valid Percent

Cumulative Percent Valid Less than high

school 6 1.5 1.5 1.5

High school 94 23.7 23.7 25.3

Bachelor degree 198 50.0 50.0 75.3

Advanced graduate work or PhD

67 16.9 16.9 100.0

Total 396 100.0 100.0

Which country are you living in? (Please specify the exact country name in English if you are liv...

Frequency Percent Valid Percent Cumulative Percent Valid China 213 53.8 53.8 53.8 United States 183 46.2 46.2 100.0 Total 396 100.0 100.0

There were also no counter indicative items in the survey, so there was no need to recode the items. Moreover, there was no need to test reliability as I use scales from existing literatures (See Table 6 for Cronbach’s Alpha), which have already been tested. Then I performed normality check of the variables involved in the hypotheses. I computed the scale means of the predictor variables in SPSS.

5.3. Hypotheses testing

The results produced by the multiple linear regression models are shown in Table 7 and 8. The results are somewhat different for the two product categories considered and for the stages of shopping. In particular, different variables influence channel choice at each shopping stage.

According to table 7, H1a and H1b are supported for both product categories, which show that usefulness is key variable positively influencing Internet usage in search and purchase

communication for consumer electronics during evaluation stage. H3b is fully supported. The result shows that when customers are shopping for concrete problem solving, nowadays, they consider multiple touchpoints important in the process both online and in store. Furthermore, H4a is partially supported as I find that ease-of-use is only positively related to the use of online channels for restaurant in search stage. H4b is also partially supported as the effect is only significant for consumer electronics category. Both H5a and H5b are not supported showing that customers do not use online channel because they enjoy the process of using online channel despite product category. H6a is not supported showing that people enjoying shopping the product do not necessarily prefer online channel less despite product category. H6b is only supported for consumer electronics category. Lastly, H7a is not supported for both categories while H7b are supported for both categories indicating that having a lot of knowledge or involvement with the product category does not necessarily drive people to search information online, however, it does make people willing to pay online.

Table 7 Multiple linear regression for online versus store touchpoint preference at search and purchase stage of shopping journey Consumer

Electronics Information Search Purchase

Independent variables

(Online/Store) Non-stand. Beta coeff. Stand. Beta coeff. t Non-stand. Beta coeff. Stand. Beta coeff. t

Constant 0.862 - 1.975 0.493 - 1.162 Extrinsic motivations Usefulness 0.415 0.306 4.838*** 0.45 0.342 5.393*** Time pressure -0.037 -0.03 -0.676 -0.02 -0.017 -0.377 Intrinsic motivations Ease-of-use 0.159 0.112 1.506 0.247 0.18 2.404** Enjoyment -0.03 -0.024 -0.375 -0.048 -0.039 -0.607

Socio-demographic variables Gender (reference: Male) -0.249 -0.11 -2.517** -0.387 -0.175 -4.015*** Age -0.091 -0.088 -2.119** -0.062 -0.061 -1.475 Education 0.001 0.002 0.39 0.053 0.065 1.495 Country (reference: China) 0.848 0.38 7.668*** 0.63 0.291 5.852*** Adjusted R2 0.329 0.326

Restaurant Information Search Purchase

Independent

variables Non-stand. coeff. Stand. coeff. t Non-stand. coeff. Stand. coeff. t

Constant 0.898 - 2.102** 2.254 - 4.906*** Extrinsic motivations Usefulness 0.179 0.144 2.095** 0.312 0.24 3.391*** Time pressure 0.016 0.014 0.295 -0.012 -0.01 -0.21

Intrinsic motivations Ease-of-use 0.231 0.178 2.173** -0.127 -0.094 -1.112 Enjoyment -0.021 -0.018 -0.25 0.097 0.08 1.085 Hedonic orientation -0.001 -0.001 -0.017 0.052 0.043 0.701 Product involvement 0.028 0.023 0.412 0.126 0.099 1.71* Socio-demographic variables Gender (reference: Male) 0.055 0.026 0.569 -0.179 -0.082 -1.737* Age -0.098 -0.103 -2.265** -0.077 -0.078 -1.662 Education 0.013 0.017 0.351 0.066 0.081 1.651 Country (reference: China) 0.574 0.281 5.427*** -0.652 -0.305 -5.731***

Table 8 Multiple linear regression for direct and indirect touchpoint importance at evaluation stage of shopping journey

Consumer Electronics

In-store

communication Observe others

Use mobile to read online reviews in store

In-Website

communication Online Reviews

Independent variables (Direct/Indirect) Stand. coeff. t Stand. coeff. t Stand. coeff. t Stand. coeff. t Stand. coeff. t Constant - 6.412 - 7.193*** - 5.417*** - 6.627 - 5.168*** Extrinsic motivations Product Utilitarian Value 0.028 0.587 0.092 2.002* 0.156 3.101*** 0.173 3.364*** 0.243 5.135*** Socio-demographic variables Gender (reference: Male) 0.082 1.7 0.053 1.15 0.082 1.618 0.114 2.275** 0.166 3.482*** Age 0.167 3.497*** 0.01 0.216 -0.059 -1.187 -0.025 -0.496 -0.075 -1.599 Education 0.025 0.506 0.065 1.344 0.035 0.677 0.055 1.057 0.067 1.361 Country (reference: China) -0.302 -5.996*** -0.413 -8.546*** 0.044 0.838 0.05 0.955 0.218 4.4*** Adjusted R2 0.105 0.177 0.024 0.033 0.132

Restaurant

In-store

communication Observe others

Use mobile to read online reviews in store

In-Website

communication Online Reviews

Independent variables Stand. coeff. t Stand. coeff. t Stand. coeff. t Stand. coeff. t Stand. coeff. t Constant - 8.087*** - 8.107*** - 6.627*** - 5.198*** - 8.156 Extrinsic motivations Product Utilitarian Value 0.121 2.44** 0.251 5.175*** 0.169 3.401*** 0.221 4.48*** 0.161 3.258*** Socio-demographic variables Gender (reference: Male) 0.114 2.308** -0.01 -0.212 0.142 2.866 0.177 3.598*** 0.18 3.65*** Age 0.028 0.565 -0.043 -0.89 -0.117 -2.386** -0.001 -0.026 -0.128 -2.616** Education -0.041 -0.801 0.062 1.225 -0.049 -0.944 0.035 0.686 0.007 0.131 Country (reference: China) -0.251 -4.877*** -0.181 -3.583*** 0.056 1.093 0.127 2.472** 0.092 1.791

Table 9 Overall Summary of tested hypotheses

Hypothesis Description Conclusion

H1a

A higher level of perceived usefulness is positively associated to the preference of the online (vs. store) for search.

Both categories supported

H1b

A higher level of perceived usefulness is positively associated to the preference of the online (vs. store) for purchase.

Both categories supported

H2a

A higher shopper time pressure is positively associated to the preference of the online (vs. the store) for search.

Both categories NOT supported

H2b

A higher shopper time pressure is negatively associated to the preference of the online (vs. the store) for

purchase. Both categories NOT supported H3a

The higher perceived product utilitarian value is positively associated to the perceived importance of direct touchpoints for evaluation both online and in store. In-store communicat ion for consumer electronics NOT supported; Other supported H3b

The higher perceived product utilitarian value is positively associated to the importance of indirect touchpoints for evaluation both online and in store.

Both categories supported

H4a

A higher level of perceived ease-of-use is positively associated to the preference of the online (vs. the store) for search.

Restaurant category supported

H4b

A higher level of perceived ease-of-use is positively associated to the preference of the online (vs. the store) for purchase. Consumer Electronics category supported H5a

A higher level of perceived enjoyment is positively associated to the use of the online (vs. the store) for search Both categories NOT supported H5b

A higher level of perceived enjoyment is positively associated to the use of the online (vs. the store) for purchase. Both categories NOT supported H6a

A higher shopper hedonic orientation is negatively associated to the use of the online (vs. the store) for search.

Both categories NOT supported

H6b

A higher shopper hedonic orientation is negatively associated to the use of the online (vs. the store) for purchase. Consumer Electronics category supported H7a

A higher product involvement is positively associated to the use of the online (vs. the offline) for search.

Both categories NOT supported

H7b

A higher product involvement is positively associated to the use of the online (vs. the offline) for purchase.

Both categories supported

6. CHAPTER 6 – DISCUSSION AND CONCLUSION

6.1.Discussion

My study contributes to the current literature on multichannel consumer behavior in the two following ways. First, I demonstrate that touchpoint choice is explained by different variables at each of the three phases of the purchase process considered (i.e. search, evaluation and purchase). This adds considerably to the literature, as

touchpoint preference for search has been seldom considered and touchpoint

importance for evaluation, to the best of our knowledge, has never been investigated. Second, we show that product category affects the preference of touchpoints at each stage. My research framework is based on an extension of the TAM Model

considering the Motivational Model to account for a variety of extrinsic and intrinsic motivations to explain channel usage, and on transaction/information costs economics to explain different channel usage at each shopping stage and for each product

category. Our main conclusion is that the variables investigated influence channel usage differently at each purchase stage and for each product category. The only variable explaining a higher use of online channels across the shopping process and for both product categories is perceived usefulness: those shoppers considering online channels effective for shopping use the online touchpoints more to search and

purchase. Focusing on each specific stage there are more common variables in the two categories: for information search, only usefulness is common variable in the two categories. For purchase, the common variables are product involvement and

usefulness. For evaluation stage, there is evidence that despite online or store, customers, who shop for problem solving, consider direct and indirect touchpoints important for product choice decisions. This is another evidence of touchpoint

combination. The differences between categories point out that intrinsic motivations such as ease-of-use and hedonic orientation are more relevant when purchasing electronics than when purchasing for restaurant. Extrinsic motivations such as usefulness equally explain channel preference in both categories.

6.2.Managerial Implication

Our research findings have valuable implications for channel migration campaigns of multichannel retailers. If retailers want to persuade customers to search online they should continue improving the usefulness of their websites for finding the products, provide value-added information on the products and allow easy comparison of alternatives. Particularly, retailers must work on the perceived usefulness of their websites for searching as our results show that searching online is clearly driven by this extrinsic motivation. To encourage online searching, Restaurant owners should promote the usefulness and ease-of-use aspect of shopping online as they are the driver of online searching. This could be achieved by simplifying the user experience of the website by showing menus with sharp pictures and clear interface to make sure customers can find what they want efficiently. For electronics retailers to encourage online purchasing it would be more important to increase the perceived usefulness of the website than to invest in making the shopping process more enjoyable. This could be achieved by providing full information about the products, reviews by other customers and expert advice or useful apps or tips for getting the maximum performance from electronic devices. Lastly, for companies selling products that